Optimal Operation and Control of Refrigeration Processes (including LNG Plants) September 26, 2003.
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Transcript of Optimal Operation and Control of Refrigeration Processes (including LNG Plants) September 26, 2003.
Optimal Operation and Control of Refrigeration Processes(including LNG Plants)
September 26, 2003
September 26 2003 2
Outline
The basic refrigeration cycle Other refrigeration processes Where is refrigeration applied? Energy saving by improved operation or control Optimal operation and control LNG plants Summary
Acknowledgments
References
September 26 2003 3
The Basic Refrigeration Cycle
(Dossat, 1991)
A
Condensation BC
Vaporization
D
Specific enthalpy
Log pressure
p1
p2
Expansion Compression
GasGas andLiquid
Liquid
Compressor
Evaporator
Motor
Expansion valve
Condenser
Receiver
Cooledstream out
Q in
Q out
C
DA
B
September 26 2003 4
Operation and Control of Refrigeration Processes
Main output: cooled stream outlet temperature
Main input: compressor effect
Several internal variables that must/may be be controlled: Pressure (and thereby temperature)
before compressor Evaporator level
Possible control inputs Expansion valve opening Heat transfer in condenser Cooled stream flow rate Refrigerant composition
Compressor
Evaporator
Motor
Expansion valve
Condenser
Receiver
Cooledstream out
Q in
Q out
TT
Power
Compressor
Evaporator
Motor
Expansion valve
Condenser
Receiver
Cooledstream out
Q in
Q out
TT
Power
LT
PT
September 26 2003 5
A Typical Control Structure
Compressor
Evaporator
Motor
Expansion valve
Condenser
Receiver
Cooledstream out
Q in
Q out
TT
Power
LT
PTLC
TC
SIC
September 26 2003 6
Other Refrigeration Processes
Multiple stages refrigeration
Open liquefaction cycle:
liquefied gas is withdrawn as product, replaced by dry gas (e.g. air)
Absorption refrigeration – no compressor needed
(e.g. gas refrigerators)
(Wilson and Jones, 1994)
Condenser
Receiver
Evaporators
September 26 2003 7
Where Is Refrigeration Applied?
Refrigerators and freezers in homes, warehouses, hospitalsProcessing and transport of foodAir conditioningHeat pumps (efficient heating by cooling the environment)Process industry whenever cooling water temperature is not
sufficientLiquefaction and separation of air: oxygen, nitrogen, argonLiquefaction of gases: LNG, hydrogen, helium, chlorine, …Re-liquefaction (ship gas transport)Conventional superconductors
– Particle accelerator (e.g. CERN), 1.9K Rocket fuel: liquid hydrogen and oxygen
September 26 2003 8
Energy Saving by Improved Control or Operation
EU, 1990: the total electricity consumption for refrigeration in the food industry was estimated at 8TWh/year (Norway’s total electrical energy production 2002: 122TWh/year)
Centre for Analysis and Dissemination of Demonstrated Energy Technologies (CADDET). Improved control examples:
– Gilde, Norway: run the “correct” compressors (5% savings)– Inghams Enterprises, Somerville (Australia): avoid compressor cycling
(966MWh/year)– Rainier Cold Storage, Port of Seattle: compressors adjusted after load and
environmental changes (367MWh/year)
Process control 30%
Computer controlledspeed fans 30-44%
Computer aided operation: 20%
Energy savings in demonstration projects:
September 26 2003 9
Optimal Operation and Control
In the industry: optimal means improvedA solution that maximizes (or minimizes) a criterionCriterion?
– In the end: Maximize profit– Maximize throughput– Minimize cost, i.e. total power consumption or power
consumption per produced unitFree variables?Constraints?Process modelTypical disturbances:
– Varying cooling demand– Compressor upsets– Varying heat-transfer in condenser
0
min
pF
Pp
pp
September 26 2003 10
Operation? Control?
Optimal operation = optimal steady state working point
Operation may also involve– maintenance of equipment– manual interventions– turnarounds
but these are not covered here
Optimal control = optimal way to reach this working point and handle disturbances
– Linear Quadratic Gaussian Control (LQG)– Model Predictive Control (MPC)
September 26 2003 11
Skogestad and Postletwaite (1996)
Control
Operation
Optimal
Control Hierarchy
September 26 2003 12
What Can Be Gained With Optimal Operation…
less compressor recycling less suction temperature overheating higher suction pressure increased cooled stream temperature more effective cooling cyclewith more than one compressor: improved power distributionconnected to other process units (e.g. pumps and fans): improved
power distribution between the units
A
Condensation BC
Vaporization
D
Specific enthalpy
Log pressure
p1
p2
Expansion Compression
GasGas andLiquid
Liquid
Compressor
Evaporator
Motor
Expansion valve
Condenser
Receiver
Cooledstream outQ in
Q out
C
DA
B
September 26 2003 13
… and with Optimal Control?
the process is kept at optimum (despite disturbances)
transients are optimal the margins can be reduced
the optimum can be improved
y
y
y
yref
yref
yref
September 26 2003 14
Air Separation Units
Produce oxygen, nitrogen and argon from airAir is liquefied with a nitrogen refrigeration cycleSeparation of the components with distillation columnsHigh purity requirements
Main control and operational challenges: the distillation columnsSchenk et al. (2002): Simultaneous optimal design of
– process (number of trays and diameter)– control structure (pairing of outputs and inputs)– controller tuning
1.5 days of CPU time
September 26 2003 15
LNG Plants
Natural gas cooled to below -163°C– Liquefied at 1atm
Volume reduction with a factor of 600Possible to transport gas with ships
– Alternative to pipe transport
September 26 2003 16
Optimal Operation of LNG Plants
Main objectives:Maximize LNG production
or Minimize storage
Minimize energy consumption
September 26 2003 17
Optimal Control of LNG Refrigeration Plants (Mandler et al.,1998)
Main control objectives– Maintain a set LNG production rate– Maintain the LNG temperature within a desired range
Other control objectives depend on the process configuration
Constraints– Input ranges (valve ranges, power limits, compressor limits and rate
change limits)
– Process output ranges (suction pressures, relief valve settings, distance to compressor surge, …)
September 26 2003 18
Snøhvit LNG Plant (Norway)
Gas produced at the gas fields Snøhvit, Albatross and AskeladdSubsea production160 km of piping into the LNG plantProduction: 5.7 billion Sm3 LNG/year 2006-2035Operated by Statoil ASA
September 26 2003 19
LNG, Mixed Fluid Cascade Process (simplified)
Precooling
Liquefaction
Subcooling
NG
LNG
Sea water
Sea water
Sea water
-160°C
-50°C
-80°C
September 26 2003 20
Basic Control strategy
Precooling
Liquefaction
Subcooling
NG
FIC
TIC
TIC
TIC
LNG
PIC
PIC
PIC
September 26 2003 21
Operation
Adjust to obtaindesired production rate
Subcooling
NG
FIC
TIC
TIC
TIC
LNG
PIC
PIC
PICT1
T2
Precooling
Liquefaction
P1
P2
P3
Specified
September 26 2003 22
Optimal Operation, an Exercise
Objective: Minimize energy consumption in the 3 compressors
Free variables: Compressor suction pressures, P1, P2, and P3
Other free variables:
– Temperatures T1 and T2
– Refrigerant composition in each cycle (nitrogen, methane, ethane, propane, …)
Some constraints: – LNG production rate and temperature– Flow into compressor shall be gas– Compressor constraints
September 26 2003 23
Optimization
Optimizationserver
(SQP)
Adjust freevariables
Results
User interface(Excel)
Model
(Hysys)
Optimizationproblem definition
Objective functionand constraint
values
When available:Measurements
September 26 2003 24
Results: Optimal Operation
Changing the suction temperature margin from 10 to 5°C:
Increase in suction pressure
P1 0.63 bar
P2 0.61 bar
P3 0.84 bar
Compressor consumption: 103 -> 93 MW
Savings: 10MW (=0.09TWh/year)
September 26 2003 25
Optimal Control, Snøhvit
Potential for savings with optimal control are not fully determined:– the actual disturbances are unknown– recycle of vaporized NG during ship loading– steady gas production? – composition variations?– regular pre-treatment?– compressor shut-downs?
Preliminary dynamic study (with disturbances as expected)– Low potential for savings identified– Exceptions
during large production level changes during start-up
Will try to start without optimal control Regulatory control shall be sufficient for stable and safe operation
September 26 2003 26
Optimal Control: Possible Solution
Optimization criterion– Maximize LNG flow rate– Minimize energy consumption in the compressors
Possible manipulated variables:
– NG temperatures after 1st and 2nd heat exchanger (T1, T2 )– Set-point for refrigerant flow in subcooler – Set-point for LNG temperature– Refrigerant compositions
Constraints as before Additional measurements:
– NG inlet flow rate– NG inlet composition
Statoil MPC, SEPTIC (planned to be used in to control columns in the pre-treatment processes)
September 26 2003 27
GL2Z LNG Plant in Arzew, Algeria (Zaïm, 2002)
6 identical liquefaction trains Product delivered to ships Optimization in two levels
1. Plantwide optimization: Minimize storage and thereby– storage loss– production cost (produce as
little as possible)
2. Maximize process efficiency of each train
September 26 2003 28
Arzew, Algeria: Plantwide Optimization (Zaïm, 2002)
Adapt the LNG production to the downstream demand (i.e. ships arrivals and capacities)
Inputs– Ship loading schedule– Plan for maintenance of trains– Product quality requirements– Feed gas composition
Method– Define time intervals with constant demand– Determine required production in each train for each interval– Feedback from measured production
September 26 2003 29
Optimal Control of Each Train (Zaïm, 2002)
Obtain desired – production rate– product quality
Minimize energy consumption Other outputs to be controlled
– two refrigerant temperatures in the main heat exchanger– pressures after the two expansion valves
Control inputs– Natural gas composition and flow– Mixed refrigerant composition and flow
Model Predictive Control No simulation results available
September 26 2003 30
Summary
The cooling cycle: Compression, condensation, expansion, vaporization Control challenges:
– Avoid liquid in the compressor– Inverse response in the evaporator
Refrigeration: Many important applications– at home and the food industry– process industry (liquefaction)
Energy demanding Optimal operation and control
– Minimize energy consumption and fulfil constraints– Identified potentials for savings (e.g. reduce compressor cycling)– Up to 30-40% of the energy consumption can be reduced
LNG plants: Liquefaction of natural gas– Two examples of optimal operation
September 26 2003 31
Acknowledgments
Colleagues at Statoil ASA– Pål Flatby, John-Morten Godhavn, Silja E. Gylseth, Oddvar
Jørstad, Håvard Nordhus, Jørgen Opdal, Geir A. Owren, Jan Richard Sagli
Dag Eimer, former colleague at Norsk Hydro ASATerje Herzberg, Dept. of Chemical Engineering, NTNUMorten Hovd, Dept. of Engineering Cybernetics, NTNUStaff at the NTNU Library
September 26 2003 32
References (1)
Refrigeration TextbooksDossat, R. J. (1991), Principles of refrigeration, 3rd ed., Prentice-Hall International Editions, London.
Flynn, Th. (1997), Cryogenic Engineering, Marcel Dekker, Inc., New York.
Haselden, G. G. (ed.), Cryogenic fundamentals, Academic Press, London.
Energy Consumption and EfficiencyEU: http://europa.eu.int/comm/energy_transport/atlas/htmlu/refrigeration.html
Grandum, S. and Eriksen, K. (2000), Control system minimizes energy use in a meat-processing factory, CADDET Energy Efficiency News Bulletin, No.3, pp. 16-17
Inghams Enterprises (2002), Advanced Food Refrigeration Control, CADDET web page, http://www.caddet-ee.org
Rainier Cold Storage, Inc. (2000), Improved Refrigeration Control System in A Food Cold Storage Facility, CADDET web page, http://www.caddet-ee.org
The Norwegian Water Resources and Energy Directorate (NVE) The energy folder 2002, http://www.nve.no/
September 26 2003 33
References (2)
Refrigeration Process ControlBalchen, J. G. and Mummé, K. I. (1988), Process control. Structures and applications., Van Nostrand
Reinhold, New York.
Balchen, J. G., Telnes, K. and Di Ruscio, D. (1989), Frequency response adaptive control of a refrigeration cycle, Modeling, Identification and Control (MIC), Vol.10, No.1, pp. 3-11.
Esnoz, A. and Lopez, A. (2003), Fuzzy logic PI controller with on-line optimum intermediate pressure for double stage refrigeration system, 21st IIR International Congress of Refrigeration, August 17-22, 2003, Washington, DC, USA.
Goldfarb, S. and Oldham, J. (1996), Refrigeration loop dynamic analysis using PROTISS, ESCAPE-6, 26-29 May 1996, Rhodes, Greece; Supplement to Computers & Chemical Engineering, Vol. 20, pp. S811-S816
Langley, B. C. (2002), Fine tuning Air Conditioning & Refrigeration Systems, The Fairmont Press Inc., Lilburn, GA.
Lensen, B. A. (1991), Improve control of cryogenic gas plants, Hydrocarbon Processing, May, 1991, pp. 109-111
Marshall, S.A. and James, R. W. (1975), Dynamic analysis of an industrial refrigeration system to investigate capacity control, Proc. Inst. Mech. Engrs., Vol. 189, No.44/75, pp. 437-444
Wilson, J.A. and Jones, W.E. (1994), The influence of plant design on refrigeration circuit control and operation, ESCAPE-4, Dublin March 28-30, '94, pp. 215-221.
September 26 2003 34
References (3)
Optimal Operation and Control (see also applications and LNG)Chen, J. (1997), Optimal Performance analysis of irreversible cycles used as heat pumps and
refrigerators, J. Phys. D: Appl. Phys., Vol. 30, pp. 582-587
D’Accadia, M. D., Sasso, M. and Sibilio, S. (1997), Optimum performance of heat engine-driven heat pumps: A finite-time approach, Energy Convers. Vol. 38, No. 4, pp. 401-413
Diaz, S., Tonelli, S., Bandoni, A. and Biegler, L.T. (2003), Dynamic Optimization for Switching Between Operating Modes in Cryogenic Plants, FOCAPO 2003. 4th Int. Conf. of Computer-Aided Process Operations, Proceedings of the Conference held at Coral Springs, Florida, January 12-15, 2003, pp. 601-604
Leducq, D., Guilpart, J. and Trystram, G. (2003), Application of a reduced dynamic model to the control of a refrigeration cycle, 21st IIR International Congress of Refrigeration, August 17-22, 2003, Washington, DC, USA.
Mandler, J.A. (1998), Modeling for Control Analysis and Design in Complex Industrial Separation and Liquefaction Processes, DYCOPS-5, 5th IFAC Symposium on Dynamics and Control of Process Systems, Corfu, Greece, June 8-10, 1998, pp. 405-413.
Schenk, M., Sakizlis, V., Perkins, J.D. and Pistikopoulos E.N. (2002), Optimization-Based Methodologies for Integrating Design and Control in Cryogenic Plants, European Symposium on Computer Aided Process Engineering - 12, 26-29 May 2002, The Hague, The Netherlands, pp.331-336.
Svensson, Ch., M. (1994), Studies on on-line optimizing control, with application to a heat pump, Ph.D. thesis, Dept. of Refrigeration and Air Conditioning, Norwegian University of Science and Technology, Trondheim, Norway
September 26 2003 35
References (4)
Refrigeration Operation and Control ApplicationsAlvarez, G. and Trystram, G. (1995), Design of a new strategy for the control of the refrigeration process:
fruit and vegetables conditioned in a pallet, Food Control, Vol. 6, No. 6, pp. 347-355.
Andersen, J. (2002), Temperature control in the large Hadron Collider at CERN, M.Sc. Thesis, Dept. of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, Norway
Cho, C. H. and Norden, N. (1982), Computer Optimization of Refrigeration Systems in a Textile Plant: A Case History, Automatica, Vol.18, No. 6, pp. 675-683.
Flemsæter, B. (2000), Investigation, modelling and control of the 1.9K cooling loop for superconducting magnets for the large hadron collider, Ph.D. thesis, Dept. of Refrigeration and Air Conditioning, Norwegian University of Science and Technology, Trondheim, Norway
Hokanson, D. A., Houk, B.G. and Johnston, Ch., R. (1989), DMC Control of a complex refrigerated fractionator, Adv. Instum. Control, pp. 541-552.
Kaya, A. (1991), Improving efficiency in existing chillers with optimization technology, ASHRAE Journal, October 1991, pp. 30-38
Luong, T.T.H. and Pham, Q.T. (2003), Multi-objective optimization of food refrigeration processes, 21st IIR International Congress of Refrigeration, August 17-22, 2003, Washington, DC, USA.
Martin, M., Gannon, J. Rode, C. and McCarthy, J. (1981), Quasi-optimal algorithms for the control loops of the FERMILAB energy saver satellite refrigerator, IEEE Transactions of Nuclear Science, Vol. NS-28, No. 3, June, pp. 3251-3253
Olson, R.T. and Liebman, J.S.(1990), Optimization of a chilled water plant using sequential quadratic programming, Eng.Opt., Vol. 15, pp.171-191.
Skimmeli, T. (1994), Control of Refrigeration Process at Dalgård (Indoor) Ice Rink, Master thesis, Department of Engineering Cybernetics, Norwegian University of Science and Technology
Trelea, I.-C., Alvarez, G. and Trystram, G. (1997), Nonlinear predictive optimal control of a batch refrigeration process, J. Food Process Engn., Vol. 21, pp.1-32.
September 26 2003 36
References (5)
LNG and Control of LNG plantsMandler, J.A. and Brochu, P.A. (1997), Controllability Analysis of the LNG Process, Presented at 1997
AIChE Annual Meeting, Los Angeles, CA (Paper 197a)
Mandler, J.A., Brochu, P.A., Fotopoulos, J. and Brochu, P.A. (1998), New Control Strategies for the LNG Process, Presented at LNG 12 Conference, Perth, Australia, May 1998
The Snøhvit project: www.statoil.com/snohvit
Zaïm, A. (2002), Dynamic optimization of an LNG plant. Case study: GL2Z LNG plant in Arzew, Algeria, Ph.D. Thesis, Rheinisch-Westfälishen Technischen Hochschule (RWTH), Aachen, Shaker Verlag, Aachen.
Other Sources for the PresentationCERN: http://public.web.cern.ch/public/
Gram Refrigerators: http://www.gram.dk/produkter.htm
Skogestad, S. and Postletwaite, I. (1996), Multivariable feedback control, John Wiley & Sons, Chichester, UK
September 26 2003 37
Refrigeration Operation and Control Applications
Process industry– NLG plant (Diaz, S. et al., 2003)
– Multivariable control (DMC) of a fractionator with a refrigeration process (Hokanson et al.,1989)
– Nylon plant: Steady state optimization of 8 cycles (Cho et al., 1982)
Food– Control for fruits and vegetables (Alvarez and Trystram, 1995)
– Steady state optimization (Luong and Pham, 2003)Air condition
– Optimal operation (Olson and Liebman, 1990, Kaya, 1991)Particle accelerators
– FERMILAB (USA) (Martin, 1981)
– CERN (Europe) (Flemsæter, 2000, Andersen, 2002)Other Applications
– New control structures for indoor ice rinks (Skimmeli, 1994)